Find parameters for building urban communities

source('../env.R')

taxonmomic_mapping = read_csv(filename(TAXONOMY_OUTPUT_DIR, 'taxonomy_mapping.csv'))

Rows: 438 Columns: 7── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): species_name, birdlife_common_name, ebird_common_name, ebird_species_name, jetz_species_name
dbl (2): birdlife_id, ebird_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Find sample size, and percentage of checklists (or percentage of effort) for valid urban communities based on presence test data.

Load abundance data

abundance_data = read_csv(filename(EBIRD_WORKING_OUTPUT_DIR, 'ebird_trends_abundance_test_data.csv'))

Rows: 30876 Columns: 7── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): species, city_name
dbl (5): ID, breeding, nonbreeding, resident, city_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

species_available = unique(abundance_data$species)

Create regional pool data

inspection_cities = c('Manchester', 'Bogota', 'Los Angeles', 'Jakarta', 'Nairobi')

regional_pool_data_input = read_csv(filename(BIRDLIFE_WORKING_OUTPUT_DIR, 'urban_distributions.csv'))

Rows: 7215 Columns: 6── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): species_name, city_name, origin, seasonal, presence
dbl (1): city_id
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

regional_pool_data_input$seasonal = factor(regional_pool_data_input$seasonal, levels = c('Resident', 'Breeding Season', 'Non-breeding Season', 'Passage', ordered = T))
regional_pool_data_input[regional_pool_data_input$city_name %in% inspection_cities,] 

regional_pool_data = regional_pool_data_input %>% 
  filter(species_name %in% species_available) %>%
  arrange(seasonal) %>%
  group_by(city_id, city_name, species_name) %>%
  summarise(seasonal = first(seasonal)) %>%
  arrange(city_id, species_name)

`summarise()` has grouped output by 'city_id', 'city_name'. You can override using the `.groups` argument.

regional_pool_data[regional_pool_data$city_name %in% inspection_cities,]

Filter abundance data to regional pools

abundance_data_regional = left_join(regional_pool_data, abundance_data, by = c('city_id' = 'city_id', 'city_name' = 'city_name', 'species_name' = 'species'))
abundance_data_regional = abundance_data_regional %>% arrange(city_id) %>% select(city_id, city_name, species_name, seasonal, breeding, nonbreeding, resident)

abundance_data_regional[abundance_data_regional$city_name %in% inspection_cities,]

Are any species now missing that are abundant?

city_id_species_pairs_present = paste(abundance_data_regional$species_name, abundance_data_regional$city_id, sep = '::')
abundance_data %>% 
  filter(breeding > 0 | nonbreeding > 0 | resident > 0) %>% 
  group_by(city_id, species) %>% 
  filter(!(paste(species, city_id, sep = '::') %in% city_id_species_pairs_present)) %>%
  select(city_id, city_name, species, breeding, nonbreeding, resident)

Create urban summary data

Get number of checklists and total effort in each city

urban_checklists = read_csv(filename(EBIRD_WORKING_OUTPUT_DIR, 'urban_checklists.csv'))

Rows: 3542050 Columns: 6── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): checklist_id, locality_id, city_name
dbl  (2): city_id, duration
date (1): obs_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

urban_summary = urban_checklists %>% group_by(city_id, city_name) %>% summarise(total_city_checklists = n(), total_city_effort = sum(duration))

`summarise()` has grouped output by 'city_id'. You can override using the `.groups` argument.

urban_summary[urban_summary$city_name %in% inspection_cities,]

Create urban pools

For each of our test species, find the number of checklists and total effort in each city

ebird_raw = read_csv(filename(EBIRD_WORKING_OUTPUT_DIR, 'urban_columbidae.csv'))

Rows: 2593785 Columns: 8── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): observation_count, checklist_id, city_name, species_name, common_name
dbl (3): number_observers, city_id, duration
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(ebird_raw)

Join eBird data to taxonomy, and then ensure species only appear on each checklists. e.g. deduplicate any species that had duplicates created by taxonomy mapping.

urban_columbidae_raw_ebird_records = 
  left_join(ebird_raw, taxonmomic_mapping, by = c('species_name' = 'ebird_species_name')) %>%
  filter(species_name.y %in% species_available) %>%
  group_by(checklist_id, city_id, city_name, species_name.y) %>%
  summarise(
    duration = max(duration)
  ) %>%
  rename(species_name = species_name.y) %>%
  arrange(city_name)

`summarise()` has grouped output by 'checklist_id', 'city_id', 'city_name'. You can override using the `.groups` argument.

urban_columbidae_raw_ebird_records[urban_columbidae_raw_ebird_records$city_name %in% inspection_cities, ]

Now calculate the total number of checklists each species appears on, and then total effort of those checklists. This gives a single row for each species in each city. Then join the data onto the regional pool data, and the urban summary data.

urban_ebird_columbidae_city_summary = urban_columbidae_raw_ebird_records %>% 
  group_by(city_id, city_name, species_name) %>% 
  summarise(total_presence_checklists = n(), total_presence_effort = sum(duration)) %>%
  left_join(regional_pool_data, by = c('city_id' = 'city_id', 'city_name' = 'city_name', 'species_name' = 'species_name')) %>%
  left_join(urban_summary, by = c('city_id' = 'city_id', 'city_name' = 'city_name')) %>%
  arrange(city_id, species_name)

`summarise()` has grouped output by 'city_id', 'city_name'. You can override using the `.groups` argument.

NA for seasonal indicates that the species is on an eBird checklist for the city, but not in the birdlife regional pool.

urban_ebird_columbidae_city_summary[urban_ebird_columbidae_city_summary$city_name %in% inspection_cities,]

We also want records for each city that could have that pigeon (e.g. in regional pool range) but has no eBird records. Create absence records for all species in the birdlife regional pool data for all cities.

urban_columbidae_absence_records_part1 = left_join(regional_pool_data, urban_summary)

Joining, by = c("city_id", "city_name")

urban_columbidae_absence_records_part1$total_presence_checklists = 0
urban_columbidae_absence_records_part1$total_presence_effort = 0

urban_columbidae_absence_records = urban_columbidae_absence_records_part1[!is.na(urban_columbidae_absence_records_part1$total_city_checklists),]

urban_columbidae_absence_records[urban_columbidae_absence_records$city_name %in% inspection_cities,]

Append the absence and presence data together, and then deduplicate to ensure only one species per city. Then calculate the percentage of all checklists, and the percentage of total effort in a city for each species.

urban_columbidae_presence_and_absence = rbind(urban_ebird_columbidae_city_summary, urban_columbidae_absence_records) %>%
  group_by(city_id, city_name, species_name, total_city_checklists, total_city_effort, seasonal) %>%
  summarise(total_presence_checklists = max(total_presence_checklists), total_presence_effort = max(total_presence_effort)) %>%
  arrange(city_id, species_name)

`summarise()` has grouped output by 'city_id', 'city_name', 'species_name', 'total_city_checklists', 'total_city_effort'. You can override using the `.groups` argument.

urban_columbidae_presence_and_absence$percentage_checklists = urban_columbidae_presence_and_absence$total_presence_checklists * 100 / urban_columbidae_presence_and_absence$total_city_checklists
urban_columbidae_presence_and_absence$percentage_effort = urban_columbidae_presence_and_absence$total_presence_effort * 100 / urban_columbidae_presence_and_absence$total_city_effort

urban_columbidae_presence_and_absence[urban_columbidae_presence_and_absence$city_name %in% inspection_cities,]

Join all data together

Finally join our our test data result to show predicted abundance, remove any NA abundance as there is insufficent data in these places for a prediction.

columbidae = left_join(urban_columbidae_presence_and_absence, abundance_data, by = c('species_name' = 'species', 'city_id' = 'city_id', 'city_name' = 'city_name'))
columbidae[columbidae$city_name %in% inspection_cities,]

Plot our species pools for each inspection city

plot_city_species = function(city_id) {
  city_name = urban_summary$city_name[urban_summary$city_id == city_id]
  total_checklists = urban_summary$total_city_checklists[urban_checklists$city_id == city_id]
  
  ggplot(columbidae[columbidae$city_id == city_id,], aes(x = species_name, y = percentage_checklists, fill = seasonal)) + 
    geom_bar(stat = "identity") + 
    geom_text(aes(label = paste('breeding', round(breeding, 1), '\nresident', round(resident, 1)))) + 
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
    xlab('Species name') + ylab('% of Checklist present') + 
    facet_wrap(~city_id) + 
    geom_hline(yintercept=5, linetype='dotted', col = 'red') +
    labs(title = paste(city_name, total_checklists))
}

plot_city_species(14)

plot_city_species(624)

plot_city_species(1786)

plot_city_species(4830)

plot_city_species(11920)

Attempt to predict abundance from percentage of checklists present

ggplot(columbidae[columbidae$species_name == 'Columba palumbus',], aes(y = percentage_checklists, x = breeding, color = log(total_city_checklists))) + geom_jitter() + geom_jitter() + scale_colour_gradient2(
  low = "red",
  mid = "yellow",
  high = "darkgreen",
  midpoint = log(100),
  space = "Lab",
  na.value = "grey50",
  guide = "colourbar",
  aesthetics = "colour",
)

ggplot(columbidae, aes(y = percentage_checklists, x = sqrt(breeding), color = log(total_city_checklists))) + geom_jitter() + geom_jitter() + scale_colour_gradient2(
  low = "red",
  mid = "yellow",
  high = "darkgreen",
  midpoint = log(100),
  space = "Lab",
  na.value = "grey50",
  guide = "colourbar",
  aesthetics = "colour",
) + geom_smooth()

Outliers

columbidae[!is.na(columbidae$breeding) & sqrt(columbidae$breeding) > 3.5,]

ggplot(columbidae, aes(y = percentage_checklists, x = sqrt(resident), color = log(total_city_checklists))) + geom_jitter() + geom_jitter() + scale_colour_gradient2(
  low = "red",
  mid = "yellow",
  high = "darkgreen",
  midpoint = log(100),
  space = "Lab",
  na.value = "grey50",
  guide = "colourbar",
  aesthetics = "colour",
) + geom_smooth()

Outliers

columbidae[!is.na(columbidae$resident) & sqrt(columbidae$resident) > 5,]

Try mapping max abundance

columbidae$max_abundance = apply(columbidae[,c('breeding', 'nonbreeding', 'resident')], 1, max, na.rm = T)

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columbidae$max_abundance[columbidae$max_abundance == -Inf] = NA

ggplot(columbidae[!is.na(columbidae$max_abundance) & sqrt(columbidae$max_abundance) < 5,], aes(y = percentage_checklists, x = sqrt(max_abundance), color = log(total_city_checklists))) + geom_jitter() + geom_jitter() + scale_colour_gradient2(
  low = "red",
  mid = "yellow",
  high = "darkgreen",
  midpoint = log(100),
  space = "Lab",
  na.value = "grey50",
  guide = "colourbar",
  aesthetics = "colour",
) + geom_smooth()

linear_model = lm(percentage_checklists ~ sqrt(max_abundance), columbidae[!is.na(columbidae$max_abundance) & sqrt(columbidae$max_abundance) < 5,])
summary(linear_model)

Call:
lm(formula = percentage_checklists ~ sqrt(max_abundance), data = columbidae[!is.na(columbidae$max_abundance) & 
    sqrt(columbidae$max_abundance) < 5, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-82.863  -8.993  -0.402   4.268  98.897 

Coefficients:
                    Estimate Std. Error t value             Pr(>|t|)    
(Intercept)          -1.7677     0.4639  -3.811             0.000141 ***
sqrt(max_abundance)  18.8411     0.3226  58.402 < 0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 17.08 on 3238 degrees of freedom
Multiple R-squared:  0.513, Adjusted R-squared:  0.5128 
F-statistic:  3411 on 1 and 3238 DF,  p-value: < 0.00000000000000022

plot(linear_model)

linear_model = lm(percentage_checklists ~ max_abundance, columbidae[!is.na(columbidae$max_abundance) & sqrt(columbidae$max_abundance) < 5,])
summary(linear_model)

Call:
lm(formula = percentage_checklists ~ max_abundance, data = columbidae[!is.na(columbidae$max_abundance) & 
    sqrt(columbidae$max_abundance) < 5, ])

Residuals:
     Min       1Q   Median       3Q      Max 
-114.636   -8.561   -7.391    7.133   91.915 

Coefficients:
              Estimate Std. Error t value            Pr(>|t|)    
(Intercept)     7.9623     0.3956   20.13 <0.0000000000000002 ***
max_abundance   5.2871     0.1082   48.85 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18.57 on 3238 degrees of freedom
Multiple R-squared:  0.4243,    Adjusted R-squared:  0.4241 
F-statistic:  2386 on 1 and 3238 DF,  p-value: < 0.00000000000000022

plot(linear_model)

trimmed_data = columbidae[!is.na(columbidae$max_abundance) & sqrt(columbidae$max_abundance) < 5,]
seeds = c(123, 456, 678, 10, 11, 345, 32, 11, 54, 90, 9999, 1234, 5678, 2323, 9011, 532, 111, 678, 6501, 3)

# min_city_checklists is the minimum number of cities a checklist must have to be included.
# get_model is a function that takes a dataframe subset of `columbidae`, and returns the result of `lm`
# get_actual is a function that returns the expected result of the prediction given a dataframe subset of `columbidae`
get_mean_test_error = function(min_city_checklists, get_model, get_expected) {
  data = trimmed_data[trimmed_data$total_city_checklists >= min_city_checklists,]
  
  result = data.frame()
  
  for(seed in seeds) {
    set.seed(seed)
    train = sample(1:nrow(data), nrow(data)/2)
    test=(-train)
    
    model = get_model(data[train,])
    prediction = predict(model, data[test,])
    
    result = rbind(result, data.frame(prediction = prediction, actual = data$max_abundance[test]))
  }

  sum((result$prediction - result$actual)^2) / nrow(result)
}

min_city_checklists = seq(0, 5000, by=10)

First test predicting abundance directly.

result = data.frame()

for (min_city_checklist in min_city_checklists) {
  result = rbind(result, data.frame(
    min_city_checklist = min_city_checklist,
    mean_test_error = get_mean_test_error(
      min_city_checklist, 
      function(training_data) {lm(max_abundance ~ percentage_checklists, training_data)},
      function(test_data) {test_data$max_abundance})
  ))
}

ggplot(result, aes(x = min_city_checklist, y = mean_test_error)) + geom_line()

Second test predicting the sqrt of abundance.

result = data.frame()

for (min_city_checklist in min_city_checklists) {
  result = rbind(result, data.frame(
    min_city_checklist = min_city_checklist,
    mean_test_error = get_mean_test_error(
      min_city_checklist, 
      function(training_data) {lm(sqrt(max_abundance) ~ percentage_checklists, training_data)},
      function(test_data) {sqrt(test_data$max_abundance)})
  ))
}

ggplot(result, aes(x = min_city_checklist, y = mean_test_error)) + geom_line()

Third test predicting the presence.

trimmed_data$nonzero_abundance = trimmed_data$max_abundance > 0

trimmed_data$total_city_checklists_bin = cut(trimmed_data$total_city_checklists, breaks = 25, labels = F)
trimmed_data$total_city_checklists_bin = as.factor(trimmed_data$total_city_checklists_bin)
ggplot(trimmed_data, aes(x = total_city_checklists_bin, y = percentage_checklists)) + geom_boxplot() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + facet_wrap(~ nonzero_abundance)

get_true_false_test_results = function(min_city_checklists) {
  data = trimmed_data[trimmed_data$total_city_checklists >= min_city_checklists,]
  
  result = data.frame()
  coeeffs = c()
  
  for(seed in seeds) {
    train = sample(1:nrow(data), nrow(data) / 2)
    test=(-train)
    
    model = glm(nonzero_abundance ~ percentage_checklists, "binomial", data[train,])
    prediction = predict(model, data[test,], type = "response")
    
    result = rbind(result, data.frame(actual = data$nonzero_abundance[test], predicted = prediction))
    coeeffs = append(coeeffs, model$coefficients['percentage_checklists'])
  }

  data.frame(
    mean_false_probability = mean(result$predicted[!result$actual]),
    mean_true_probablility = mean(result$predicted[result$actual]),
    percentage_checklists_cutoff = mean(coeeffs),
    min_city_checklists = min_city_checklists
  )
}

result = data.frame()

for (min_city_checklist in min_city_checklists) {
  result = rbind(result, get_true_false_test_results(min_city_checklist))
}

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probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted probabilities numerically 0 or 1 occurredWarning: glm.fit: fitted 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ggplot(result, aes(x = min_city_checklists, y = mean_false_probability)) + geom_line()

result[result$min_city_checklists %in% c(500, 1000, 1500, 2000),]